Sequence labeling involves algorithmic assignment of a categorical label to each value of a sequence of observed values. An example of sequence labeling is “part of speech tagging,” in which a part of speech is assigned to each word in a series of words (e.g., each word in a sentence of a document).
Sequence labeling may incorporate statistical models, such as the hidden Markov model (HMM), to choose a label for a particular word. In such models, the choice of a label for a particular word may depend on labels chosen for adjacent words.
For example, in a typical sentence of a document, certain words may be unambiguously labeled as having a particular part of speech (e.g., the word “the” may unambiguously be labeled as a determiner), which, in turn may increase or decrease the probability that another word (e.g., to the immediate left or right of the labeled word) may be accurately labeled as belonging to another particular part of speech.
However, the application of such sequence labelling techniques to search queries may be more difficult than applying such labelling techniques to typical sentences used in speech. For example, search queries consist of series of keywords that may or may not form a complete sentence. Thus, it may be more difficult to ascertain the relationship between the keywords to one another.